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  4. 中科院海岸带环境过程与生态修复重点实验室
Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS

文献类型：期刊论文

作者Wang, Mengxia4,5; Fu, Boya4; Fan, Jianbo3; Wang, Yi2; Zhang, Liankuan1; Xia, Chunlei4
刊名ECOLOGICAL INFORMATICS
出版日期2023-03-01
卷号73页码:12
ISSN号1574-9541
关键词Dense leaf detection Object detection CBAM Occlusion detection Plant leaves Leaf counting
DOI10.1016/j.ecoinf.2022.101931
通讯作者Xia, Chunlei(clxia@yic.ac.cn)
英文摘要Accurate detection of plant leaves is a meaningful and challenging task for developing smart agricultural sys-tems. To improve the performance of detecting plant leaves in natural scenes containing severe occlusion, overlapping, or shape variation, we developed an in situ sweet potato leaf detection method based on a modified Faster R-CNN framework and visual attention mechanism. First, a convolutional block attention module was added to the backbone network to enhance and extract critical features of leaf images by fusing cross-channel information and spatial information. Subsequently, the DIoU-NMS algorithm was adopted to modify the regional proposal network by replacing the original NMS. DIoU-NMS was utilized to reduce missed and incorrect detection in scenes of densely distributed leaves by considering the targets' overlap ratio, distance, and scale. The proposed leaf detection method was tested and evaluated on sweet potato plant images collected in agricultural fields. In the datasets, sweet potato leaves were presented in various sizes and poses, and a large proportion of leaves were occluded or overlapped with each other. The experimental results showed that the proposed leaf detection method outperforms state-of-the-art object detection methods. The mean average precision of the proposed method reached 95.7%, which was 2.9% higher than that of the original Faster R-CNN and 7.0% higher than that of YOLOv5. The proposed method achieved promising performance in detecting dense leaves or occluded leaves and could provide key techniques for applications in smart agriculture and ecological moni-toring, such as growth monitoring or plant phenotyping.
WOS关键词IDENTIFICATION
WOS研究方向Environmental Sciences & Ecology
语种英语
WOS记录号WOS:000895767300007
资助机构National Key Research and Develop-ment Program of China ; Shandong Province Key R&D Program (Major Science and Technology Innovation Project)
源URL[http://ir.yic.ac.cn/handle/133337/32407]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_山东省海岸带环境工程技术研究中心
通讯作者Xia, Chunlei
作者单位1.South China Agr Univ, Coll Math & Informat, Guangzhou 510642, Peoples R China
2.Naval Aeronaut Univ, Coastal Def Coll, Yantai 264003, Peoples R China
3.Yantai Ctr Serv Conf & Exhibiting Ind, Yantai 264003, Peoples R China
4.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China
5.Yantai Univ, Dept Phys & Elect Informat, Yantai 264005, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Mengxia,Fu, Boya,Fan, Jianbo,et al. Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS[J]. ECOLOGICAL INFORMATICS,2023,73:12.
APA Wang, Mengxia,Fu, Boya,Fan, Jianbo,Wang, Yi,Zhang, Liankuan,&Xia, Chunlei.(2023).Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS.ECOLOGICAL INFORMATICS,73,12.
MLA Wang, Mengxia,et al."Sweet potato leaf detection in a natural scene based on faster R-CNN with a visual attention mechanism and DIoU-NMS".ECOLOGICAL INFORMATICS 73(2023):12.

入库方式： OAI收割

来源：烟台海岸带研究所

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